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视觉工作记忆表征维持的神经机制研究
其他题名The neural mechanism of load effect on maintaining visual working memory representations
潘亚丽
学位类型博士
导师王亮
2018-06
学位授予单位中国科学院研究生院
学位授予地点北京
学位专业认知神经科学
关键词视空工作记忆 表征维持 记忆负载效应 相位同步 频间祸合
摘要

    人类的视觉系统可以同时接收到外界环境中的多种视觉信息,但是只有有限数量的信息能够得到进一步的加工,并以记忆表征的形式暂时存储于工作记忆之中,以便指导后续的认知活动。以往的功能磁共振研究发现多个脑区形成的较大的脑网络,其中包括额顶网络以及颗枕区,都参与对视觉工作记忆表征的维持,那么在记忆负载(即记忆项目数)逐渐增加的过程中,这些脑区是怎样完成对记忆表征的维持呢?
    研究一将采用头皮脑电技术,利用其较高时间分辨率的优势,从时间进程的角度阐述不同的脑区是如何维持多个工作记忆表征的,研究发现在工作记忆的表征维持过程中,额叶、顶叶、颗叶和枕叶都参与其中,记忆负载对这些脑区的脑电幅值的影响也不尽相同,在时间上体现出了较强的动态变化性,晚期阶段的脑电活动能够更好的预测个体的行为表现。研究二则进一步的采用具有更高时间和空间分辨率的颅内脑电技术,从频率信息的角度来探究这个问题,结果发现在记忆表征的维持阶段,额叶具有持续增强的高频神经活动,而顶叶在较低频率上有持续的负激活,感觉区的电极在较低频率上则表现出先显著负激活,然后显著正激活的模式,这种负正切换的激活模式受到工作记忆负载的调节,体现出低频功率值的功能抑制作用。接下来在研究三中,从功能连接的角度探讨不同的脑区之间是如何协同合作进行表征维持的,结果发现主要通过de lta频段的相位同步性活动实现的,并且负载效应对不同脑区间功能连接的影响各不相同。最后,在研究四中通过频间祸合的计算,发现顶叶电极的de lta频段相位和gamma频段幅值之间的祸合受到工作记忆负载的调节,符合神经振荡的嵌套模型的理论假设,证明该模型能够很好的解释视空工作记忆表维持过程中的神经活动。
    总的来说,多个脑区共同参与到多物体记忆表征维持这一复杂的认知过程中构成一个分布式的脑网络,在时间和频率上都有较强的动态性,彼此之间通过低频相位信息的同步来协同合作,顶叶区域的相位幅值祸合现象符合神经振荡嵌套模型的理论假设。

其他摘要

    The human visual system can receive multiple visual stimuli simultaneously from the environment, but only a limited amount of information can be further processed into the working memory as memory representations, in order to guide the follow-up cognitive activities. Previous fMRI studies have found that multiple brain regions including the front-parietal network and the temporal occipital region, all involved in the maintenance of visual working memory representations. And how do these brain regions maintain the memory representations during the gradual increase in memory load (i.e., number of memory items)?
    The first study used scalp EEG, taking the advantage of its higher time resolution,explored how different brain regions maintain the representations of multiple working memory in time domain, and found that the frontal lobe, parietal lobe, temporal lobe and occipital lobe were all involved in the maintenance. The effect of memory load on the EEG amplitude of these brain regions changed dynamically. Only EEG activity in the late delay stage could better predict individual's performance. In the second study,intracranial EEG technology with higher temporal and spatial resolution was further adopted, which added rich frequency information. We found that the frontal lobe showed continuous enhancement of high-frequency neural activity in the maintenance stage of working memory representations, and the parietal lobe had sustained negative activation at lower frequencies.  At lower frequencies,  the sensory region first exhibited a significant negative activation, then switched to a significant positive activation, which was regulated by working memory load and embodied the function role of low-frequency power, that was gating by inhibition. The third study explored the functional connectivity between different brain regions during representations' maintenance. The results showed a significant delta band phase synchronization, which was also modulated by the working memory load, with higher load level induced longer phase synchronization. Finally in the fourth study, we did a cross frequency coupling analysis and found a significant load effect on the delta band phase and gamma band amplitude coupling in parietal, with lower load level inducing stronger coupling. This result was in line with the nested neuronal oscillation model. Therefore, this model provided a good interpretation of the neural activity during working memory representations' maintenance.
    In general, multiple brain regions participated in the complex cognitive process of multiple memory representations, constructed a distributed brain network with strong dynamics in both the time and frequency domain, and communicated with each other through low-frequency phase synchronization. The phase amplitude coupling of the parietal activity was in line with the nested neuronal oscillation model

语种中文
文献类型学位论文
条目标识符http://ir.psych.ac.cn/handle/311026/26097
专题认知与发展心理学研究室
作者单位中国科学院心理研究所
推荐引用方式
GB/T 7714
潘亚丽. 视觉工作记忆表征维持的神经机制研究[D]. 北京. 中国科学院研究生院,2018.
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